Computer vision neural network system

ABSTRACT

A computer vision neural network system is provided. The computer vision neural network system includes a computer with a multi-attribute loss module that concatenates first and second normalized feature vector descriptors to generate a master feature vector descriptor that describes attributes and classes of a facial image. The multi-attribute loss module estimates an error distance between the master feature vector descriptor and a plurality of class center vector descriptors. Each class center vector descriptor is a mean of a plurality of master feature vector descriptors associated with a soft-biometric class in a plurality of faces in a plurality of facial images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/729,194 filed on Sep. 10, 2018, the entire contents of which arehereby incorporated by reference herein.

BACKGROUND

For identifying a similarity of a facial image to a textual descriptionor another facial image, the inventors herein utilize a master featurevector descriptor which is an encoding of the attributes and featurespresent in the facial image.

The inventors herein have conceived determining an improved errordistance value utilizing a multi-attribute loss module. As result, theerror distance is back-propagated through neural networks in a computervision neural network system to update weight values in the neuralnetworks to improve the accuracy of an encoding/determination of amaster feature vector descriptor.

SUMMARY

A computer vision neural network system in accordance with an exemplaryembodiment is provided. The computer vision neural network systemincludes a computer having a facial segmentation module, a featureextraction module, a semantic segmentation masking module, a facialfeatures vectors module, a normalization module, and a multi-attributeloss module. The facial segmentation module receives a facial image andpredicts a plurality of facial regions in the facial image. The facialsegmentation module generates a plurality of masks based on theplurality of facial regions. Each mask of the plurality of masks isassociated with a respective facial region of the plurality of facialregions. The feature extraction neural network receives the facial imagehaving the face therein, and applies a plurality of learned filter banksto the facial image to generate a plurality of feature maps. Thesemantic segmentation masking module applies each mask of the pluralityof masks to the plurality of feature maps to obtain at least a firstplurality of feature maps corresponding to a first facial region, and asecond plurality of feature maps corresponding to a second facialregion. The plurality of facial regions include the first and secondfacial regions. The facial features vectors module has at least firstand second neural networks that receive the first and second pluralityof feature maps, respectively, from the semantic segmentation maskingmodule. The first neural network performing dimensionality reduction ofthe first plurality of feature maps associated with the first facialregion to generate a first feature vector descriptor associated with thefirst facial region. The second neural network performing dimensionalityreduction of the second plurality of feature maps associated with thesecond facial region to generate a second feature vector descriptorassociated with the second facial region. The normalization modulenormalizes the first and second vector descriptors to generate first andsecond normalized feature vector descriptors. The multi-attribute lossmodule concatenates the first and second normalized feature vectordescriptors to generate a master feature vector descriptor. Themulti-attribute loss module estimates an error distance between themaster feature vector descriptor and a plurality of class center vectordescriptors. Each class center vector descriptor is a mean of aplurality of master feature vector descriptors associated with asoft-biometric class in a plurality of faces in a plurality of facialimages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of a computer vision neural network system 20 inaccordance with an exemplary embodiment;

FIGS. 2-6 are flowcharts of a method for determining an error distanceutilizing a multi-attribute loss module between a master feature vectordescriptor and a plurality of class center vector descriptors, utilizingthe computer vision neural network system 20 of FIG. 1;

FIG. 7 is a block diagram of modules and neural networks utilized by thecomputer vision neural network system 20 of FIG. 1 including a facialsegmentation module, a feature extraction neural network, a semanticsegmentation masking module, a facial features vectors module, anormalization module, a multi-attribute loss module, and a localizer andclassifier module

FIG. 8 is a block diagram of a feature extraction neural network thatgenerates a plurality of feature maps associated with a facial image;

FIG. 9 is a block diagram of a facial segmentation module that generatesa plurality of masks based on a plurality of facial regions in a facialimage, and a semantic segmentation masking module that applies each maskof the plurality of masks to a plurality of feature maps to obtainupdated feature maps;

FIG. 10 is a block diagram of the plurality feature maps, the facialfeatures vectors module, the normalization module, and a master featurevector descriptor associated with the facial image; and

FIG. 11 is a block diagram of an exemplary neural network.

DETAILED DESCRIPTION

Referring to FIGS. 1 and 7, a computer vision neural network system 20in accordance with an exemplary embodiment is provided. The computervision neural network system 20 includes a computer 30, an imagedatabase 40, an input device 50, and a display device 60.

The computer 30 is operably coupled to the input device 50, the displaydevice 60, and image database 40. The computer 30 includes a facialsegmentation module 350, a feature extraction neural network 360, asemantic segmentation masking module 370, a facial features vectorsmodule 380, a normalization module 390, a multi-attribute loss module400, and a localizer and classifier module 410.

The input device 50 is provided to receive user selections forcontrolling operation of the computer 30. The display device 60 isprovided to display images in response to instructions received from thecomputer 30. The image database 40 is provided to store a plurality offacial images therein.

An advantage of the computer vision neural network system 20 is that thesystem 20 determines an error distance utilizing a novel multi-attributeloss function. As a result, the system 20 can utilize the error distanceto update weighting values in neural nodes in neural networks in thesystem 20 to more accurately determine master feature vector descriptorsthat describe the attributes and features in facial images.

For purposes of understanding, a few technical terms used herein willnow be explained.

The term “dimensionality reduction” is a process of reducing the numberof random variables under consideration, by obtain a set of principlevariables.

The term “feature” is a recognizable pattern that is consistentlypresent in a facial image. An exemplary feature is a hair style.

The term “attribute” is an aggregate of a set of features determined bya plurality of features in a facial image. An exemplary attribute ishair.

The term “class” is a subset or value of an attribute. An exemplarysoft-biometric class is black hair.

Referring to FIGS. 2-7, a flowchart of a method for determining an errordistance utilizing the multi-attribute loss module 400 between a masterfeature vector descriptor and a plurality of class center sectordescriptors, utilizing the computer vision neural network system 20 willnow be explained.

At step 200, the facial segmentation module 350 (shown in FIG. 7)receives a facial image 340 (shown in FIG. 7) and predicts a backgroundregion, a hair region, a face-skin region, an eyes region, an eyebrowsregion, a lips region, and a nose region in the facial image 340. Afterstep 200, the method advances to step 202.

At step 202, the facial segmentation module 350 generates a backgroundmask 500 (shown in FIG. 9), a hair mask 502, a face-skin mask 504, aneyes mask 506, an eyebrows mask 508, a lips mask 510, and a nose mask512 based on the background region, the hair region, the face-skinregion, the eyes region, the eyebrows region, the lips region,respectively. After step 202, the method advances to step 204.

At step 204, the facial segmentation module 350 sends the backgroundmask 500, the hair mask 502, the face-skin mask 504, the eyes mask 506,the eyebrows mask 508, the lips mask 510, and the nose mask 512associated with the facial image 340 to the semantic segmentationmasking module 370 (shown in FIG. 7). After step 204, the methodadvances the step 206.

At step 206, the feature extraction neural network 360 (shown in FIG. 8)receives the facial image 340, and applies a plurality of learned filterbanks 450 (shown in FIG. 8) to the facial image 340 to generate aplurality of feature maps 460 associated with the facial image 340.After step 206, the method advances to step 208.

At step 208, the feature extraction neural network 360 sends theplurality of feature maps 460 associated with the facial image 340 tothe semantic segmentation masking module 370 (shown in FIG. 7). Afterstep 208, the method advances to step 210.

At step 210, the semantic segmentation masking module 370 applies thebackground mask 500 (shown in FIG. 9) to the plurality of feature maps460 (shown in FIG. 9) associated with the facial image 340 to obtain afirst plurality of feature maps 600 (shown in FIG. 9) corresponding tothe background region. After step 210, the method advances to step 212.

At step 212, the semantic segmentation masking module 370 applies thehair mask 502 (shown in FIG. 9) to the plurality of feature maps 460(shown in FIG. 9) associated with the facial image 340 to obtain asecond plurality of feature maps 602 (shown in FIG. 9) corresponding tothe hair region. After step 212, the method advances to step 220.

At step 220, the semantic segmentation masking module 370 applies theface-skin mask 504 (shown in FIG. 9) to the plurality of feature maps460 (shown in FIG. 9) associated with the facial image 340 to obtain athird plurality of feature maps 604 (shown in FIG. 9) corresponding tothe face-skin region. After step 220, the method advances to step 222.

At step 222, the semantic segmentation masking module 370 applies theeyes mask 506 (shown in FIG. 9) to the plurality of feature maps 460(shown in FIG. 9) associated with the facial image 340 to obtain afourth plurality of feature maps 606 (shown in FIG. 9) corresponding tothe eyes region. After step 222, the method advances to step 224.

At step 224, the semantic segmentation masking module 370 applies theeyebrows mask 508 (shown in FIG. 9) to the plurality of feature maps 460(shown in FIG. 9) associated with the facial image 340 to obtain a fifthplurality of feature maps 608 (shown in FIG. 9) corresponding to theeyebrows region. After step 224, the method advances to step 226.

At step 226, the semantic segmentation masking module 370 applies thelips mask 510 (shown in FIG. 9) to the plurality of feature maps 460associated with the facial image 340 to obtain a sixth plurality offeature maps 610 (shown in FIG. 9) corresponding to the lips region.After step 226, the method advances to step 228.

At step 228, the semantic segmentation masking module 370 applies thenose mask 512 (shown in FIG. 9) to the plurality of feature maps 460(shown in FIG. 9) associated with the facial image 340 to obtain aseventh plurality of feature maps 612 (shown in FIG. 9) corresponding tothe nose region. After step 228, the method advances to step 230.

At step 230, the semantic segmentation masking module 370 (shown inFIGS. 7 and 9) sends the first, second, third, fourth, fifth, sixth, andseventh plurality of feature maps 600, 602, 604, 606, 608, 610, 612 tothe facial feature vectors module 380 (shown in FIG. 7) and thelocalizer and classifier module 410 (shown in FIG. 7). In an exemplaryembodiment, each of the first, second, third, fourth, fifth, sixth, andseventh plurality of feature maps 600, 602, 604, 606, 608, 610, 612include 78 feature maps with a height of 7 and a width of 6. The facialfeatures vectors module 380 has first, second, third, fourth, fifth,sixth, and seventh neural networks 700, 702, 704, 706, 708, 710, 712(shown in FIG. 10). After step 230, the method advances the step 232.

At step 232, the first neural network 700 (shown in FIG. 10) performs adimensionality reduction of the first plurality of feature maps 600(shown in FIG. 10) associated with the background region to generate afirst feature vector descriptor 750 (shown in FIG. 10) associated withthe background region. After step 232, the method advances to step 240.

At step 240, the second neural network 702 (shown in FIG. 10) performs adimensionality reduction of the second plurality of feature maps 602(shown in FIG. 10) associated with the hair region to generate a secondfeature vector descriptor 752 (shown in FIG. 10) associated with thehair region. After step 240, the method advances to step 242.

At step 242, the third neural network 704 (shown in FIG. 10) performs adimensionality reduction of the third plurality of feature maps 604(shown in FIG. 10) associated with the face-skin region to generate athird feature vector descriptor 754 (shown in FIG. 10) associated withthe face-skin region. After step 242, the method advances to step 244.

At step 244, the fourth neural network 706 (shown in FIG. 10) performs adimensionality reduction of the fourth plurality of feature maps 606(shown in FIG. 10) associated with the eyes region to generate a fourthfeature vector descriptor 756 (shown in FIG. 10) associated with theeyes region. After step 244, the method advances to step 246.

At step 246, the fifth neural network 708 (shown in FIG. 10) performs adimensionality reduction of the fifth plurality of feature maps 608(shown in FIG. 10) associated with the eyebrows region to generate afifth feature vector descriptor 758 (shown in FIG. 10) associated withthe eyebrows region. After step 246, the method advances to step 248.

At step 248, the sixth neural network 710 (shown in FIG. 10) performs adimensionality reduction of the sixth plurality of feature maps 610(shown in FIG. 10) associated with the lips region to generate a sixthfeature vector descriptor 760 (shown in FIG. 10) associated with thelips region. After step 248, the method advances to step 250.

At step 250, the seventh neural network 712 (shown in FIG. 10) performsa dimensionality reduction of the seventh plurality of feature maps 612(shown in FIG. 10) associated with the nose region to generate a seventhfeature vector descriptor 762 (shown in FIG. 10) associated with thenose region. After step 250, the method advances to step 252.

At step 252, the normalization module 390 normalizes the first, second,third, fourth, fifth, sixth, and seventh feature vector descriptors 750,752, 754, 756, 758, 760, 762 to generate first, second, third, fourth,fifth, sixth, and seventh normalized feature vector descriptors 800,802, 804, 806, 808, 810, 812, respectively (shown in FIG. 10). Afterstep 252, the method advances to step 254.

At step 254, the multi-attribute loss module 400 concatenates the first,second, third, fourth, fifth, sixth, and seventh normalized featurevector descriptors 800, 802, 804, 806, 808, 810, 812 to generate amaster feature vector descriptor 900 (shown in FIG. 10). In an exemplaryembodiment, each of the first, second, third, fourth, fifth, sixth, andseventh normalized feature vector descriptors 800, 802, 804, 806, 808,810, 812 is a vector with a dimension of 52. After step 254, the methodadvances to step 256.

At step 256, the multi-attribute loss module 400 estimates an errordistance between the master feature vector descriptor 900 and aplurality of class center vector descriptors. Each class center vectordescriptor is a mean of a plurality of master feature vector descriptorsassociated with a soft-biometric class in a plurality of faces in aplurality of facial images. The error distance is calculated utilizingthe following equation:L _(MAL)=Σ_(i=1) ^(N)Σ_(j=1) ^(M)(√{square root over ((f _(i) −c_(j)))}²

wherein:

-   L_(MAL) is the error distance,-   N is a number of images in a dataset,-   M is a number of attributes,-   f_(i) a master features vector descriptor for the facial image i,-   c_(j)a class center features vector descriptor for the facial image    j,-   a ground truth label of class I for the facial image i,    and the master features vector descriptor generated in the facial    features vectors module is generated utilizing the following    equation:    f _(i) =f ₁ ⊕f ₂ ⊕f _(k) ⊕ . . . ⊕f ₇    f _(k) =F _(k)(T _(k))    wherein:-   f_(i) is a master feature vector descriptor for the facial image-   f_(k) is a features vector descriptor for region k-   k is a face region which takes values from 1 to 7-   T_(k) is a plurality of feature maps associated with a face region-   F_(k) is a function that performs dimensionality reduction on T_(k).

At step 258, the computer 30 has a plurality of neural nodes. Eachneural node has at least a first weight value. The computer 30determines an error derivative for the first weight with respect to theerror distance utilizing a chain rule equation. After step 258, themethod advances to step 260.

At step 260, the computer 30 determines an updated first weight valueutilizing a fraction of the error derivative and the first weight value,wherein the updated weight value allows the computer 30 to moreaccurately determine subsequent master feature vector descriptors. Afterstep 260, the method advances to step 262.

At step 262, the localizer and classifier module 410 predicts aplurality of probability values for a plurality of classes belonging torespective soft biometric attributes present in the face of the facialimage 340.

Referring to FIG. 11, for purposes of understanding, an overview theprocess for updating weight values in a neural network 1000 to obtainmore accurate master feature vector descriptors will be explained. Theneural network 1000 is a simplified version of a neural network in thesystem 20 that is utilized in part to determine a master feature vectordescriptor describing attributes in a facial image.

The backpropagation process takes place in a backward pass and iscomplementary to a forward pass. The process for updating weight valuesin a neural network 1000 includes the following steps (i), the forwardpass, (ii) calculating the error distance, (iii) calculating the errorderivative at each neural node, and (iv) updating the weight values withthe error derivative.

The forward pass involves the processing of the input data by passing itthrough a series of computations. The results or prediction of theneural network 1000 is calculated at the end of the forward pass.

The error distance is calculated utilizing the equation in step 256discussed above.

The neural network 1000 includes an input layer, one hidden layer, andan output layer. The input layer includes a neural node 1002. The hiddenlayer includes neural nodes 1004, 1006 having weight values W₁₂, W₁₃,respectively. The output layer includes a neural node 1008 having weightvalues W₂₄, W₃₄.

Each layer performs the following computation:y _(j) =f(x _(j))x _(j) =W _(ij) y _(t) +B _(ij)Where,

-   y_(j) is an output of the neural node r.-   f is a non-linear function also called the activation function. For    this example, a sigmoid activation function will be used:

${f(x)} = \frac{1}{1 + e^{- x}}$

-   x_(j) is an output of the neural node j-   W_(ij) is the weight value term of the neural node j that is applied    to the output of neural node i-   y_(i) is the output of the neural node i-   y_(j) is an output of the neural node j-   B_(ij) is a bias term of the neural node j that is applied to the    output of neural node i.

The error derivative or the gradient of the loss function at each neuralnode is calculated by using the chain rule of differential equations.The chain rule allows the system 20 to determine the error derivativefor a weight value w_(ij) with respect to the error distance E. Thechain rule is explained as follows:

Consider two functions Z and M for some input such that:Z=f(M)M=f(x)

The derivative

$\frac{dZ}{dx}$could be calculated by the chain rule as follows:

$\frac{dZ}{dx} = {\frac{dM}{dx}\;\frac{dZ}{dM}}$With respect to the backpropagation diagram shown in FIG. 11, the system20 calculates the error derivative for x₄ with respect to the errordistance as follows:

$\frac{\partial E}{\partial x_{4}} = {\frac{\partial y_{4}}{\partial x_{4}}\frac{\partial E}{\partial y_{4}}}$

Once the error derivatives are calculated the weight values are updatedby the following rule:

$w_{ij} = {w_{ij} - {\lambda\;\frac{\partial E}{\partial w_{ij}}}}$where

-   E is an error distance calculated by the loss function-   w_(ij) are weight/parameter values between neural nodes i and j-   λ is a learning rate

$\frac{\partial E}{\partial w_{ij}}$is an error derivative/gradient of weight values w_(if) with respect tothe error distance E.

While the claimed invention has been described in detail in connectionwith only a limited number of embodiments, it should be readilyunderstood that the invention is not limited to such disclosedembodiments. Rather, the claimed invention can be modified toincorporate any number of variations, alterations, substitutions orequivalent arrangements not heretofore described, but which arecommensurate with the spirit and scope of the invention. Additionally,while various embodiments of the claimed invention have been described,it is to be understood that aspects of the invention may include onlysome of the described embodiments. Accordingly, the claimed invention isnot to be seen as limited by the foregoing description.

What is claimed is:
 1. A computer vision neural network system,comprising: a computer having a facial segmentation module, a featureextraction module, a semantic segmentation masking module, a facialfeatures vectors module, a normalization module, and a multi-attributeloss module; the facial segmentation module receiving a facial image andpredicting a plurality of facial regions in the facial image; the facialsegmentation module generating a plurality of masks based on theplurality of facial regions, each mask of the plurality of masks beingassociated with a respective facial region of the plurality of facialregions; the feature extraction neural network receiving the facialimage having the face therein, and applying a plurality of learnedfilter banks to the facial image to generate a plurality of featuremaps; the semantic segmentation masking module applying each mask of theplurality of masks to the plurality of feature maps to obtain at least afirst plurality of feature maps corresponding to a first facial region,and a second plurality of feature maps corresponding to a second facialregion, the plurality of facial regions including the first and secondfacial regions; the facial features vectors module having at least firstand second neural networks that receive the first and second pluralityof feature maps, respectively, from the semantic segmentation maskingmodule; the first neural network performing dimensionality reduction ofthe first plurality of feature maps associated with the first facialregion to generate a first feature vector descriptor associated with thefirst facial region; the second neural network performing dimensionalityreduction of the second plurality of feature maps associated with thesecond facial region to generate a second feature vector descriptorassociated with the second facial region; the normalization modulenormalizing the first and second vector descriptors to generate firstand second normalized feature vector descriptors; the multi-attributeloss module concatenating the first and second normalized feature vectordescriptors to generate a master feature vector descriptor; and themulti-attribute loss module estimating an error distance between themaster feature vector descriptor and a plurality of class center vectordescriptors, each class center vector descriptor is a mean of aplurality of master feature vector descriptors associated with asoft-biometric class in a plurality of faces in a plurality of facialimages.
 2. The computer vision neural network system of claim 1,wherein: the plurality of facial regions include a background region, ahair region, a face-skin region, an eyes region, an eyebrows region, alips region, and a nose region in the facial image.
 3. The computervision neural network system of claim 2, wherein: the plurality of masksincludes a background mask, a hair mask, a face-skin mask, an eyes mask,an eyebrows mask, a lips mask, and a nose mask; and the facialsegmentation module generating the background mask, the hair mask, theface-skin mask, the eyes mask, the eyebrows mask, the lips mask, and thenose mask based on the background region, the hair region, the face-skinregion, the eyes region, the eyebrows region, the lips region, and thenose region, respectively.
 4. The computer vision neural network systemof claim 3, wherein: the first facial region comprises the backgroundregion, and the second facial region comprises the hair region; thesemantic segmentation masking module applying the background mask to theplurality of feature maps to obtain the first plurality of feature mapscorresponding to the background region; the semantic segmentationmasking module applying the hair mask to the plurality of feature mapsto obtain the second plurality of feature maps corresponding to the hairregion; the semantic segmentation masking module applying the face-skinmask to the plurality of feature maps to obtain a third plurality offeature maps corresponding to the face-skin region; the semanticsegmentation masking module applying the eyes mask to the plurality offeature maps to obtain a fourth plurality of feature maps correspondingto the eyes region; the semantic segmentation masking module applyingthe eyebrows mask to the plurality of feature maps to obtain a fifthplurality of feature maps corresponding to the eyebrows region; thesemantic segmentation masking module applying the lips mask to theplurality of feature maps to obtain a sixth plurality of feature mapscorresponding to the lips region; and the semantic segmentation maskingmodule applying the nose mask to the plurality of feature maps to obtaina seventh plurality of feature maps corresponding to the nose region. 5.The computer vision neural network system of claim 4, wherein: thefacial features vectors module further includes third, fourth, fifth,sixth, and seventh neural networks that receive the third, fourth,fifth, sixth, and seventh plurality of feature maps, respectively; thefirst neural network performing dimensionality reduction of the firstplurality of feature maps associated with the background region togenerate a first feature vector descriptor associated with thebackground region; the second neural network performing dimensionalityreduction of the second plurality of feature maps associated with thehair region to generate a second feature vector descriptor associatedwith the hair region; the third neural network performing dimensionalityreduction of the third plurality of feature maps associated with theface-skin region to generate a third feature vector descriptorassociated with the face-skin region; the fourth neural networkperforming dimensionality reduction of the fourth plurality of featuremaps associated with the eyes region to generate a fourth feature vectordescriptor associated with the eyes region; the fifth neural networkperforming dimensionality reduction of the fifth plurality of featuremaps associated with the eyebrows region to generate a fifth featurevector descriptor associated with the eyebrows region; the sixth neuralnetwork performing dimensionality reduction of the sixth plurality offeature maps associated with the lips region to generate a sixth featurevector descriptor associated with the lips region; and the seventhneural network performing dimensionality reduction of the seventhplurality of feature maps associated with the nose region to generateseventh feature vector descriptor associated with the nose region. 6.The computer vision neural network system of claim 5, wherein: thenormalization module normalizing the first and second feature vectordescriptors and third, fourth, fifth, sixth, and seventh feature vectordescriptors to generate the first and second normalized feature vectordescriptors and third, fourth, fifth, sixth, and seventh normalizedfeature vector descriptors, respectively.
 7. The computer vision neuralnetwork system of claim 6, wherein: the multi-attribute loss moduleconcatenates the first and second normalized feature vector descriptorsand the third, fourth, fifth, sixth, and seventh normalized featurevector descriptors to generate the master feature vector descriptor. 8.The computer vision neural network system of claim 7, wherein: thecomputer further includes a localizer and classifier module thatreceives the first, second, third, fourth, fifth, sixth and seventhplurality of feature maps from the semantic segmentation masking module;and the localizer and classifier module predicting a plurality ofprobability values for a plurality of classes belonging to respectivesoft biometric attributes present in the face of the facial image. 9.The computer vision neural network system of claim 1, wherein themulti-attribute loss module estimating the error distance utilizing thefollowing equation:L _(MAL)=Σ_(i=1) ^(N)Σ_(j=1) ^(M)(√{square root over ((f _(i) −c_(j)))}²

wherein: L_(MAL) is the error distance, N is a number of images in adataset, M is a number of attributes, f_(i) a master features vectordescriptor for the facial image i, c_(j) a class center features vectordescriptor for the facial image j,

a ground truth label of class j for the facial image i, and the masterfeatures vector descriptor generated in the facial features vectorsmodule is generated utilizing the following equation:f _(i) =f ₁ ⊕f ₂ ⊕f _(k) ⊕ . . . ⊕f ₇f _(k) =F _(k)(T _(k)) wherein: f_(i) is a master feature vectordescriptor for the facial image i, f_(k) is a features vector descriptorfor region k, k is a face region which takes values from 1 to 7, T_(k)is a plurality of feature maps associated with a face region, F_(k) is afunction that performs dimensionality reduction on T_(k).
 10. Thecomputer vision neural network system of claim 1, wherein: the computerhaving a plurality of neural units, each neural unit of the plurality ofneural units having at least a first weight value; the computerdetermining an error derivative for the first weight value with respectto the error distance utilizing a chain rule equation; and the computerdetermining an updated first weight value utilizing a fraction of theerror derivative and the first weight value, wherein the updated weightvalue allows the computer vision neural network to more accuratelydetermine the master feature vector descriptor.